def __init__(self,file,**dict):
'''
Wrapped in xplib.DBI.init()
'''
if type(file)==type([1,2,3]):
f=file
else:
format=dict['format']
f=TableIO.parse(file,format)
self.data=binindex(f)
def __init__(self,file,**dict):
'''
Wrapped in xplib.DBI.init()
'''
if type(file)==type([1,2,3]):
f=file
else:
format=dict['format']
f=TableIO.parse(file,format)
self.data=binindex(f)
def Main():
global args, out
args = ParseArg()
if args.output == "stdout":
out = sys.stdout
else:
try:
out = open(args.output, "w")
except IOError:
print >> sys.stderr, "can't open file ", args.output, "to write. Using stdout instead"
out = sys.stdout
argv = sys.argv
argv[0] = argv[0].split("/")[-1]
print >> out, "# This data was generated by program ", argv[
0], "(version %s)" % VERSION,
print >> out, "in bam2x ( https://github.com/nimezhu/bam2x )"
print >> out, "# Date: ", time.asctime()
print >> out, "# The command line is :\n#\t", " ".join(argv)
db_format = args.db_format
if len(db_format) == 1:
db_format = [db_format[0] for i in range(len(args.db))]
data = binindex()
for i, f in enumerate(args.db):
for item in TableIO.parse(f, db_format[i]):
flag = 0
for feat in data.query(item):
if args.pos:
if feat.start == item.start and feat.stop == item.stop:
flag = 1
elif feat == item: #define in Class.__cmp__
flag = 1
if not flag:
data.append(item)
data_list = []
for i in data:
data_list.append(i)
data_list.sort()
for i in data_list:
print >> out, i
def Main():
global args,out
args=ParseArg()
if args.output=="stdout":
out=sys.stdout
else:
try:
out=open(args.output,"w")
except IOError:
print >>sys.stderr,"can't open file ",args.output,"to write. Using stdout instead"
out=sys.stdout
argv=sys.argv
argv[0]=argv[0].split("/")[-1]
print >>out,"# This data was generated by program ",argv[0],"(version %s)"%VERSION,
print >>out,"in bam2x ( https://github.com/nimezhu/bam2x )"
print >>out,"# Date: ",time.asctime()
print >>out,"# The command line is :\n#\t"," ".join(argv)
db_format=args.db_format
if len(db_format)==1:
db_format=[db_format[0] for i in range(len(args.db))]
data=binindex()
for i,f in enumerate(args.db):
for item in TableIO.parse(f,db_format[i]):
flag=0
for feat in data.query(item):
if args.pos:
if feat.start==item.start and feat.stop==item.stop:
flag=1
elif feat==item: #define in Class.__cmp__
flag=1
if not flag:
data.append(item)
data_list=[]
for i in data:
data_list.append(i)
data_list.sort()
for i in data_list:
print >>out,i
def Main():
args=ParseArg()
hist_n=args.hist_n
clu_n=args.clu_n
File=args.input
#read emission matrix and store in Rpy2
print "#Reading emission matrix from"
emission=args.emission
print '\t'+emission
robjects.r("emission=read.table('"+emission+"',header=T,sep='\t')")
robjects.r("emission=emission[c(12,11,13,8,7,10,6,9,4,5,2,1,3,15,14),match(c('H3K4me3','H3K4me2','H3K4me1','H3K27me3','H3K36me3','H3K27ac','H2AZ'),colnames(emission))]")
state_n=robjects.r("dim(emission)[1]")[0] # number of chromatin state
color_state=['red','pink','purple','DarkOrange','Orange','Gold','yellow','DeepSkyBlue','ForestGreen','Green','Lime','GreenYellow','LightCyan','white','white']
#Find overall distribution of all chromatin states
print "Counting distribution of chromatin states..."
chromHMM_segment = TableIO.parse(args.segment,'bed')
#count represent overall probability distribution of all chromatin states
count=np.zeros(state_n)
num=0
for segment in chromHMM_segment:
num=num+1
i=int(segment.id[1:])
count[i-1]+=(segment.stop-segment.start)/200
print 'Reading %d segments... [for distribution of chromatin states]'%(num),'\r',
print
## read and index histone pattern data for single nucleosomes in all populations
print "Indexing histone pattern data for single nucleosomes in all populations..."
data=TableIO.parse(File,'metabed',header=True)
## generate bed file for chromatin states in nucleosomes to be uploaded in UCSC genome browser
if args.bed:
name=os.path.basename(File).split('.')[0]
outbed=open(name+"_State_browser.bed",'w')
print "## Start generate BED9 file for uploading..."
print >>outbed,'track name="ChromatinState" description="'+name+'" visibility=2 itemRgb="On"'
#print >>outbed,'chr\tstart\tend\t'+'\t'.join('P_%d'%(s+1) for s in range(clu_n))
for n,i in enumerate(data):
matrix=np.array(str(i).split('\t')[8:(8+hist_n*clu_n)],dtype="int").reshape(hist_n,clu_n,order="F") # matrix of histone patterns, row: histone, column: population
if n % 50000 == 0:
print "\tWriting %dth nucleosomes into BED9 file,\r"%(n),
line='\t'.join (str(f) for f in [i.chr,i.start,i.stop])
for k in range(clu_n):
state=histone2state(matrix.T[k],count)
color_code=','.join (str(int(f)) for f in np.array(matplotlib.colors.colorConverter.to_rgb(color_state[state-1]))*255)
print >>outbed,'\t'.join (str(f) for f in [i.chr,i.start,i.stop,'P_%d_%d'%(k+1,state),0,'.',i.start,i.stop,color_code])
line=line+'\t%d'%(state)
#print >>outbed,line
outbed.close()
sys.exit(1)
# read region information
region=args.region
chro=region.split(":")[0]
start=int(region.split(":")[1].split("-")[0])
end=int(region.split(":")[1].split("-")[1])
print "#Query region:["+chro+": %d-%d]"%(start,end)
y_nucle=0.47 #location of nucleosome line
## query data in region
dbi=binindex(data)
query=dbi.query(Bed([chro,start,end]))
## initialize figure
fig=plt.figure(figsize=(10,6))
ax = plt.subplot(111,frameon=False,yticks=[])
ax.set_xlim(start-(end-start)/6,end)
n=0
print "##Start draw nucleosomes:"
#################################################
## draw genes from y = y_nucle+0.04*(clu_n+1)
#### index the gene.tab file
print " ## drawing gene track ..."
print " ## Indexing gene.tab ..."
gene_dbi=DBI.init(args.genetab,'genebed')
print " ## query regions from gene.tab"
query_gene=gene_dbi.query(Bed([chro,start,end]))
#### determine height of gene track
bottoms=[0 for i in range(100)]
max_index=0
for i in query_gene:
index=0
while(1):
if i.start > bottoms[index]:
bottoms[index]=i.stop
if max_index<index: max_index=index
break
else:
index+=1
gene_track_number=max_index+1
gene_track_height=0.03*gene_track_number+0.02
ax.set_ylim(0.05,1+gene_track_height+0.01)
print " ## start draw gene track"
# add frame for gene track
rect=matplotlib.patches.Rectangle((start,y_nucle+0.04),end-start, gene_track_height, edgecolor='black',fill=False)
ax.add_patch(rect)
bottoms=[0 for i in range(100)]
for i in gene_dbi.query(Bed([chro,start,end])):
index=0
while(1):
if i.start > bottoms[index]:
addGeneToFig(i,ax,start,end,1,0.03*index+y_nucle+0.05)
bottoms[index]=i.stop
break
index+=1
#################################################
top_heatmap_y = 0.71+gene_track_height # the y axis value for bottom of top heatmaps
print "## Draw nucleosome tracks..."
for i in query:
n=n+1
print " Nucleosome %d\t at "%(n)+chro+": %d-%d"%(i.start,i.stop)
matrix=np.array(str(i).split('\t')[8:(8+hist_n*clu_n)],dtype="int").reshape(hist_n,clu_n,order="F") # matrix of histone patterns, row: histone, column: population
prob=np.array(str(i).split('\t')[(8+hist_n*clu_n):],dtype=float)
ax.plot([i.smt_pos,i.smt_pos],[y_nucle+0.03,y_nucle],color='r') #red nucleosome midpoint
rect=matplotlib.patches.Rectangle((i.start,y_nucle), i.stop-i.start, 0.03, color='#EB70AA') #pink nucleosome region
ax.add_patch(rect)
for j in range(clu_n):
state=histone2state(matrix.T[j],count)
state_rect=matplotlib.patches.Rectangle((i.start,y_nucle+0.04*(j+1)+gene_track_height+0.01), i.stop-i.start, 0.03, color=color_state[state-1])
ax.add_patch(state_rect)
im = OffsetImage(matrix, interpolation='nearest',zoom=10/(1+gene_track_height+0.01),cmap=plt.cm.binary,alpha=0.5)
if n<=9:
xybox=((n+0.5)/10.0,top_heatmap_y)
xy = [i.smt_pos,y_nucle+0.04*clu_n+0.03+gene_track_height+0.01]
xytext=((n+0.7)/10.0,top_heatmap_y)
c_style="bar,angle=180,fraction=-0.1"
elif n<=18:
xybox=((n-9+0.5)/10.0,0.2)
xy = [i.smt_pos,y_nucle]
xytext = ((n-9+0.7)/10.0,0.40)
c_style="bar,angle=180,fraction=-0.1"
else:
print "WARN: nucleosome number larger than 18 in this region, only plot the pattern for first 18 nucleosomes"
break
ab = AnnotationBbox(im, xy,
xybox=xybox,
xycoords='data',
boxcoords=("axes fraction", "data"),
box_alignment=(0.,0.),
pad=0.1)
ax.annotate("",xy,
xytext=xytext,
xycoords='data',
textcoords=("axes fraction", "data"),
arrowprops=dict(arrowstyle="->",connectionstyle=c_style))
#arrowprops=None)
ax.add_artist(ab)
# add mark for histone mark and regions with low confidence
for i in range(hist_n):
if prob[i]<0.6:
xy_star=tuple(map(sum,zip(xybox,(0.065,0.03*(hist_n-1-i)-0.01))))
ax.annotate("*",xy=xy_star,xycoords=("axes fraction", "data"),color='red')
ax.annotate('Nucleosome:', xy=(start-(end-start)/6, y_nucle), xycoords='data',size=12)
ax.annotate('Epigenetic Pattern:', xy=(start-(end-start)/6, 0.23+top_heatmap_y), xycoords='data',size=12)
ax.annotate(chro, xy=(start-(end-start)/6, 0.1), xycoords='data',size=12)
name=open(File).readline().split('\t')[8:(8+hist_n)]
for n,i in enumerate(name):
ax.annotate(i.split("_")[0],xy=(start-(end-start)/8, top_heatmap_y+0.03*(hist_n-1-n)),xycoords='data',size=10)
ax.annotate(i.split("_")[0],xy=(start-(end-start)/8, 0.2+0.03*(hist_n-1-n)),xycoords='data',size=10)
# flame for nucleosome and chromatin state tracks
rect=matplotlib.patches.Rectangle((start,y_nucle),end-start, 0.03, edgecolor='black',fill=False)
ax.add_patch(rect)
for k in range(clu_n):
rect=matplotlib.patches.Rectangle((start,y_nucle+0.04*(k+1)+gene_track_height+0.01),end-start, 0.03, edgecolor='grey',fill=False)
ax.add_patch(rect)
ax.annotate('Population%d'%(k+1),xy=(start-(end-start)/6, y_nucle+0.04*(k+1)+gene_track_height+0.01),xycoords='data',size=12)
# chromatin state legend
for s in range(state_n):
dist=(end-start)*1.0/state_n
length=dist*0.75
rect=matplotlib.patches.Rectangle((start+dist*s,0.1), length, 0.03, color=color_state[s])
ax.add_patch(rect)
ax.annotate(s+1,xy=(start+dist*s+length/3,0.075),xycoords='data',size=10)
ax.annotate("Chromatin states:",xy=(start,0.14),xycoords='data',size=12)
ax.add_patch(matplotlib.patches.Rectangle((start-length/6,0.07),end-start, 0.1, edgecolor='grey',fill=False))
plt.title("Region: ["+chro+": %d-%d]"%(start,end),size=14)
plt.savefig(args.output)
plt.close()